Table of Contents¶
Abstract¶
Road accidents has been a critical problem as every year more than 1.2M people die across the globle. There is a pressing need to make use of the data and understand the underlying cause of problem. Road safety issues are complex. There are significant differences in policies within and across the countries. In this analysis, the data from Metropolitan Police Department's (MPD) crash data management system (COBALT) is studied to find relationship between fatality and independent features. The crash data is for DC state.
Each year, more than 1.2 million people die across the globe due to road crashes; there is a pressing need to understand the underlying cause of the problem. As road safety issues are complex; it involves multi-sectorial ranging from the public, stakeholders to the policy makers. Significant differences exist both across and within countries and therefore policies and interventions need to be adapted to the local environment. The effectiveness of interventions requires a multi-disciplinary approach which include enforcement, engineering and psychological and education approaches. While the resources are limited, road safety interventions must not only address the sustainability of the outcomes but also the cost-effectiveness to implement and maintain it. More important, interventions must be evidence-based and can be evaluated over time before it is translated into policy. Hence, the research cannot be done in silo for better addressing the complexity of road safety issues. For sustainability, road safety interventions need to be guided and governed by policy in the implementation and development.
Pipeline¶
Data Preprocessing¶
In [20]:
data.describe()
Out[20]:
Outlier treatment¶
Age variables has lot of outliers. From the box plot values below 0 and above 85 are considered as outliers and replaced by na
In [21]:
data['age'] = np.where(data['age']<1, np.nan, data['age'])
fig = px.histogram(data, x="age", marginal="box")
fig.show()
In [50]:
data['age'] = np.where(data['age']>85, np.nan, data['age'])
Missings values detection and treatment¶
Only age column shows 30% of missing data which is replaced by 0
Column Creation¶
- Mask columns boolean fields fatal, impaired, speeding and ticket issued to 1s and 0s
- Fatality Rate - at an accident level, number of people faced fatal accidents/number of total people involved in the accidents
- Severity level - Club columns for minor, major and fatal accidents with indicator labels
- Map US states into 4 regions designated by US census
EDA¶
In this analysis, fatal is the target variable. As indicated below the dataset is heavily imbalanced which is treated futhur. Only 0.06% (~417) are fatal
In [28]:
print("Percentage of non fatal class {} and fatal class {} ".format(
round((data.groupby(['fatal']).agg({'fatal':'count'})['fatal'][0]/ data.shape[0])*100, 2),
round((data.groupby(['fatal']).agg({'fatal':'count'})['fatal'][1]/ data.shape[0])*100, 2)))
print("Percentage of non major class {} and major class {} ".format(
round((data.groupby(['major injury']).agg({'major injury':'count'})['major injury'][0]/ data.shape[0])*100, 2),
round((data.groupby(['major injury']).agg({'major injury':'count'})['major injury'][1]/ data.shape[0])*100, 2)))
print("Percentage of non minor class {} and minor class {} ".format(
round((data.groupby(['minor injury']).agg({'minor injury':'count'})['minor injury'][0]/ data.shape[0])*100, 2),
round((data.groupby(['minor injury']).agg({'minor injury':'count'})['minor injury'][1]/ data.shape[0])*100, 2)))
How many people involved in the accidents suffered injury?¶
In [52]:
crime = pd.DataFrame(data[['crime id', 'severity']].value_counts())
crime = crime.reset_index()
crime.columns = ['crime id', 'severity', 'count of people']
fig = px.histogram(crime, x="count of people", color='severity', marginal="box")
fig.show()
Which vehicle type has maximum accidents and most fatal accidents?¶
In [30]:
vhCount = pd.DataFrame(data[['vehicle type', 'severity']].value_counts())
vhCount.reset_index(inplace=True)
vhCount.columns = ['vehicle type', 'severity','count']
fig = px.bar(vhCount, x='vehicle type', y='count', color='severity',
title="Number of accidents by vehicle type",
template="simple_white")
# fig.layout.update(showlegend=False)
fig.show()
Which state people travel most to dc or within dc?¶
In [31]:
vhCount = pd.DataFrame(data[['License state']].value_counts())
vhCount
vhCount.reset_index(inplace=True)
vhCount.columns = ['License state', 'count']
fig = go.Figure(data=go.Choropleth(
locations=vhCount['License state'], # Spatial coordinates
z = vhCount['count'], # Data to be color-coded
locationmode = 'USA-states', # set of locations match entries in `locations`
colorscale = 'Reds',
colorbar_title = "Accidents",
))
fig.update_layout(
title_text = 'Accidents by State',
geo_scope='usa', # limite map scope to USA
)
fig.show()
Does the fatality rate of accidents changes for different state licenses?¶
As people are not familiar with the terrain, we observe that people holding licenses from different states have higher fatality rate
In [32]:
stateFatality = data[data['fatality rate']>0.0000]
stateFatalityAvg = stateFatality.groupby(['License state'], as_index=False).agg({'fatality rate':'mean'})
stateFatalityAvg
fig = go.Figure(data=go.Choropleth(
locations=stateFatalityAvg['License state'], # Spatial coordinates
z = stateFatalityAvg['fatality rate'], # Data to be color-coded
locationmode = 'USA-states', # set of locations match entries in `locations`
colorscale = 'Reds',
colorbar_title = "Accidents",
))
fig.update_layout(
title_text = 'Average fatality rate by State',
geo_scope='usa', # limite map scope to USA
)
fig.show()
Does age affect the fatality rate of an accident?¶
In [33]:
# fatality rate and age
fig = px.scatter(data, x="age", y="fatality rate", color='severity')
fig.show()
Which category of the persons type met with maximum accidents and suffered maximum injury?¶
In [49]:
ptCount = pd.DataFrame(data[['person type', 'severity']].value_counts())
ptCount.reset_index(inplace=True)
ptCount.columns = ['person type', 'severity','count']
# fatal = ptCount[ptCount['fatal'] == 'Y']
# nonfatal = ptCount[ptCount['fatal'] == 'N']
# # x = ptCount['person type']
# # y = ptCount['count']
fig = px.bar(ptCount, x='person type', y='count', color='severity',
title="Number of people met with an accident by person type",
template="simple_white",
category_orders={ # replaces default order by column name
"person type": ["Driver", "Passenger", "Pedestrian", "Bicyclist"]
},)
percentage = pd.crosstab(data['person type'], data['severity']).apply(lambda r: (r/r.sum())*100, axis=1)
fig.add_annotation(text="Accidents % <br> Fatal: {}, Major: {}, <br> Minor: {}".format(
(round(percentage['fatal']['Driver'], 2)),
(round(percentage['major']['Driver'], 2)),
(round(percentage['minor']['Driver'], 2))
), x="Driver", y=70000, showarrow=False)
fig.add_annotation(text="Accidents % <br> Fatal: {}, Major: {},<br> Minor: {}".format(
(round(percentage['fatal']['Passenger'], 2)),
(round(percentage['major']['Passenger'], 2)),
(round(percentage['minor']['Passenger'], 2))
), x="Passenger", y=70000, showarrow=False)
fig.add_annotation(text="Accidents % <br> Fatal: {}, Major: {},<br> Minor: {}".format(
(round(percentage['fatal']['Pedestrian'], 2)),
(round(percentage['major']['Pedestrian'], 2)),
(round(percentage['minor']['Pedestrian'], 2))
), x="Pedestrian", y=70000, showarrow=False)
fig.add_annotation(text="Accidents % <br> Fatal: {}, Major: {}, <br> Minor: {}".format(
(round(percentage['fatal']['Bicyclist'], 2)),
(round(percentage['major']['Bicyclist'], 2)),
(round(percentage['minor']['Bicyclist'], 2))
), x="Bicyclist", y=70000, showarrow=False)
# fig.layout.update(showlegend=False)
fig.show()
- Data contains 4 types of persons such as driver, passenger, pedestrian and bicyclist
- Driver contributes 77% of data entries followed by 19 % passenger, 2 % pedestrians and 0.6% bicyclist
- Although, driver has maximum entries only 0.05% accidents are fatal whereas 2.63% and 8.04% are major and minor
- Passenger suffered maximum minor injuries in the accidents.
- Pedestrian do not frequently meet with an accident but when occurred they have suffered 53% minor injuries, 24% major and 0.7% fatal injuries. They have maximum fatalities compared to other types
- Similarly, bicyclist have least entries of accidents but when met with an accident they suffered 60.82% of minor injuries 8% major injuries and 0.22% fatal injuries
Analysis according to person type¶
Driver¶
In [35]:
driver = data[data['person type'] == 'Driver']
Does age of the driver related to the severity of accidents?¶
In [36]:
driver = driver[driver['age'] >1]
fig = px.histogram(driver, x="age", color='severity', marginal="box")
fig.show()
Does vehicle type affect the severity of accidents?¶
In [37]:
vhCount = pd.DataFrame(driver[['vehicle type', 'severity']].value_counts())
vhCount.reset_index(inplace=True)
vhCount.columns = ['vehicle type', 'severity','count']
fig = px.bar(vhCount, x='vehicle type', y='count', color='severity',
title="Number of accidents by vehicle type",
template="simple_white")
# fig.layout.update(showlegend=False)
fig.show()
Passenger¶
In [38]:
passenger = data[data['person type'] == 'Passenger']
In [39]:
passenger = passenger[passenger['age'] >1]
fig = px.histogram(passenger, x="age", color='severity', marginal="box")
fig.show()
In [40]:
vhCount = pd.DataFrame(passenger[['vehicle type', 'severity']].value_counts())
vhCount.reset_index(inplace=True)
vhCount.columns = ['vehicle type', 'severity','count']
fig = px.bar(vhCount, x='vehicle type', y='count', color='severity',
title="Number of accidents by vehicle type",
template="simple_white")
# fig.layout.update(showlegend=False)
fig.show()
Pedestrian¶
In [41]:
pedestrian = data[data['person type'] == 'Pedestrian']
In [42]:
pedestrian = pedestrian[pedestrian['age'] >1]
fig = px.histogram(pedestrian, x="age", color='severity', marginal="box")
fig.show()
Model Selection¶
Random Uniform Under Sampling¶
Relationship Strengths¶
In [53]:
dython.nominal.associations(a[['severity', 'fatal', 'fatality rate','vehicle type', 'ticket issued','Region', 'impaired','age', 'speeding', 'person type']], theil_u=True)
Out[53]:
Multiple Linear Regression¶
In [46]:
X = a[['vehicle type', 'age', 'Region', 'speeding', 'person type']]
y = a[['fatality rate']]
X = pd.get_dummies(data = X)
X['age'] = X['age'].fillna(0)
import statsmodels.api as sm
X = sm.add_constant(X) ## let's add an intercept (beta_0) to our model
# Note the difference in argument order
model = sm.OLS(y, X).fit() ## sm.OLS(output, input)
predictions = model.predict(X)
# Print out the statistics
model.summary()
Out[46]:
Random Forest¶
In [48]:
getRF(a)